Micro-geographic aggregation system

ABSTRACT

The present disclosure describes systems and methods for automatically rolling-up data associated with one or more geographic units, such as ZIP+4 codes, such that the rollup comprises a minimum number of households to protect anonymity and ensure compliance with privacy regulations, while preserving variance of the underlying data associated with the geographic regions. Data attributes may include demographic data, socio-economic data, lifestyle segmentation, psychographic data, behavioral data, credit data, and other data. The rollup process may involve identifying one or more geographic units with a number of households below a minimum or threshold amount, applying filters to find candidate geographic units for rollup, scoring candidate geographic units to select best pairings for rollup, and repeating until the rollup group has at least the minimum number of households. The process may make trades off between granularity (e.g., number of households), proximity, and similarity of data attributes associated with each geographic unit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 119(e)of U.S. Provisional Application No. 61/905,021, filed Nov. 15, 2013, thedisclosure of which is hereby incorporated by reference in its entirety.

BACKGROUND

Geographic aggregation is a common approach to creating anonymous datafrom individual consumer or household data that may otherwise be tooprivate to disclose. Units of geographic aggregation can be based on avariety of classification schemes with different levels of granularity,for example state, county, city or neighborhood.

SUMMARY

Postal codes are a popular choice for geographic aggregations, both forgranularity and ease of use for marketing applications. In the UnitedStates, the US Postal Service assigns each deliverable address both a5-digit “ZIP Code™” as well as a 9-digit, ZIP+4 code (or “ZIP9”), thelatter generally defined in terms of a street address range. Many ofthese ZIP9 codes encompass only a small number of unique households.However, to protect privacy, a number of policies, rules and regulationshave been enacted which may limit or restrict the use of data for ZIP9codes which include too few households. The high degree of segmentationcontributes to more variance in demographic, socio-economic, behavioral,and other data associated with ZIP and ZIP9 codes. Such variance in thedata set may be desirable to some entities, such as marketers andcertain consumers, who value targeted offers and advertising.

The present disclosure describes systems and methods for automaticallycombining or “rolling-up” data associated with one or more geographicregion codes, in particular ZIP9 codes, such that the combination or“rollup” satisfies a minimum threshold of granularity (e.g., a minimumnumber of households) to protect anonymity and ensure compliance withprivacy regulations or policies, while preserving variance of theunderlying data associated with the geographic regions.

Some approaches to protect anonymity and ensure compliance with privacyregulations may involve suppressing or removing certain data which donot comply with privacy rules (e.g., by removing a “non-compliant” ZIP9from a data set, wherein the non-compliant ZIP9 has a number ofhouseholds less than a required minimum). Other approaches may involveapplying default values to non-compliant ZIP9s, or analyzing data at adifferent ZIP-level which more frequently satisfies minimum number ofhousehold requirements, such as the 5-digit ZIP code or the first 7digits of the ZIP+4 code.

However, such approaches may have drawbacks which can include lessaccurate overall data, loss of granularity, and/or loss of variancewhich may be of interest in certain cases where specificity of datacarries high value, such as in direct-mail marketing campaigns andsimilar targeted advertising efforts. This may be particularly true whencertain data attributes associated with a particular ZIP9 are skewed,such as the case may be with respect to household wealth which iscorrelated to ZIP9s having smaller number of households. Thus, in somecases more granular data (e.g., at the ZIP9 level or as close to theZIP9 level as permissible to maintain privacy and anonymity) is not onlymore valuable, but may even be essential in order to provide anindication of useful information that data at the ZIP5 or ZIP7 level donot provide.

The methods and processes described in the present disclosure provide analgorithm for rolling-up ZIP9 codes that trades off between granularity(e.g., to keep the number of households in a rolled-up ZIP9 group assmall as possible), proximity (e.g., to group ZIP9s which are within acertain geographic distance of each other as much as possible givenother constraints and criteria), and/or similarity of data attributesassociated with each ZIP9. Data attributes may include, but are notlimited to, demographic data, socio-economic data, psychographic data,behavioral data, credit data including aggregated and/or anonymizedcredit statistics, wealth data, and/or other similar types of data. Oneexample of socio-economic data or lifestyle segmentation which will bereferred to in examples throughout this disclosure is Experian's MOSAIC®service; however, any other segmentation data may be used as dataattributes for identifying similar ZIP9s or households.

At a high level, the rollup process executed by the micro-geographicaggregation system 100 operates by identifying one or more geographicunits (e.g., ZIP codes, ZIP5s, ZIP7s, ZIP9s, census tract data, streetaddress ranges, grid-based geographic regions corresponding to a map,and any other finite geographic area) having a number of householdsbelow a minimum or threshold amount. Then, data filters may be appliedin order to find or determine which geographic units are candidates forrollup. For example, data filtering may be applied to filter out orremove ZIP9s which are dissimilar based on one or more attributes (e.g.,average assets, MOSAIC® code, and so on). Once the geographic unitcandidates are identified, the rollup process scores the candidates andselects one or more with the best scores for rollup or pairing. Forexample, the candidate ZIP9s may be scored relative to a target ZIP,ZIP7, ZIP9 or relative to each other (e.g., pairwise) in order toidentify strong potential for rollup (e.g., combination or grouping oftwo or more ZIP9s). Once a rollup group has been created, identified, orupdated, the rollup process combines the number of households for eachconstituent geographic unit to determine a number of households in therollup group. If the number of households in the rollup group is lessthan the minimum or threshold amount, the rollup process may repeatrecursively or indefinitely until the identified geographic units havebeen rolled up into respective rollup groups with at least the minimumnumber of households.

The rollup algorithm or processes described herein can be applied to anyset of one or more target variables and one or more explanatoryvariables relating to ZIP data attributes, such that as ZIP9s areevaluated for possible combination in a rollup, the similarity and/ortrade-offs between associated target variables and associatedexplanatory variables are weighted. The examples herein will use averageassets as an example target variable, and MOSAIC® codes as an exampleexplanatory variable. However, in general any type of target variableand/or explanatory variable may be used, depending on the embodiment,the particular context, and/or a particular request from a requestingentity. A target variable is generally understood to be a continuousvariable such as a specific number or amount, while an explanatoryvariable may generally be a category or classification variable whichmay represent, for example, general characteristics or attributes ofmembers of the category or classification which may not corresponddirectly to a numeric value.

The micro-geographic aggregation system may apply the rollup process todata of different granularities, for different geographic regions,across a wide variety of data attributes. The micro-geographicaggregation system may provide a requesting entity with the ability todynamically query consumer data on the fly in a customized manner,simultaneously obscuring private data to protect anonymity whileproviding high-value and insightful data for use in intelligent andcustomized marketing. While ZIP9s are discussed herein, the systems andmethods discussed herein are equally applicable to other geographic unitcomprising groupings of households or individuals.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a map illustrating one example of challenges posed by ZIP9fragmentation for a small geographic region which the geographic unitrollup processes described herein are designed to address.

FIG. 2 is a flowchart of a process for rolling up one or more geographicunits, such as ZIP9 codes, together into a rollup group, using themicro-geographic aggregation system of FIG. 8.

FIG. 3 is a flowchart of a process for combining one or more geographicunits, such as ZIP9 codes, together into one or more rollup groups,using the micro-geographic aggregation system of FIG. 8.

FIG. 4 is a flowchart of a process for identifying, scoring andselecting one or more geographic units, such as ZIP9 codes, forinclusion in a rollup group, using the micro-geographic aggregationsystem of FIG. 8.

FIG. 5A illustrates an example user interface which enables an end user,such as a third party requesting entity, to submit customized rollupfilters and data requests, as generated using the micro-geographicaggregation system of FIG. 8.

FIG. 5B illustrates an example user interface which enables an end user,such as a third party requesting entity, to view rollup maps and/oroutput lists generated in response to a rollup request, as using themicro-geographic aggregation system of FIG. 8.

FIG. 6 illustrates an example geographic unit rollup output which may beprovided by the rollup processes described herein, using themicro-geographic aggregation system of FIG. 8.

FIG. 7 illustrates an example of how different map data sources may havedifferent and/or conflicting map latitude/longitude (“lat/long”)coordinates for geographic units, such as ZIP codes, which may need tobe reconciled to improve accuracy of a geographic unit rollup process.

FIG. 8 is a block diagram of an implementation of an illustrativemicro-geographic aggregation system.

DETAILED DESCRIPTION

Embodiments of a micro-geographic aggregation system will now bedescribed with reference to accompanying figures, wherein like numeralsrefer to like elements throughout. The terminology used in thedescription presented herein is not intended to be interpreted in anylimited or restrictive manner, simply because it is being utilized inconjunction with a detailed description of certain specific embodimentsof the disclosure. Furthermore, embodiments of the disclosure mayinclude several novel features, no single one of which is solelyresponsible for its desirable attributes or which is essential topracticing the embodiments of the disclosure herein described.

Overview

Generally described, a micro-geographic aggregation system is a computersystem that automatically and optimally determines combinations,aggregations, or “rollups” of geographic units, such as ZIP9 codes, inorder to satisfy a minimum or threshold number of households which maybe required, for example, to preserve anonymity of the constituenthouseholds associated with each ZIP code.

One example embodiment of a micro-geographic aggregation system is themicro-geographic aggregation system 100 shown in FIG. 8. Themicro-geographic aggregation system 100 may include one or moresub-systems, engines, or modules such as a geographic unit mappingengine 121, a geographic unit rollup engine 122, and/or a user interfacemodule 123, configured to perform the methods and processes describedherein. The micro-geographic aggregation system 100 may also access orbe in communication with one or more data sources, including geographicunit/mapping data sources 166A and/or consumer data sources 166B. Eachof these systems and components is described in more detail withreference to FIG. 8 herein.

FIG. 1 is an example map that illustrates certain challenges posed bygeographic units (e.g., ZIP9s) of disparate size and othercharacteristics for a small geographic region which the geographic unitrollup processes described herein are designed to address. The map showsseveral ZIP9 area codes for a particular region, each of which has beenoutlined to show the varying number of households within each ZIP9.Corresponding to each ZIP9 is an average quantity of assets for thatZIP9 (which may be known, for example, based on consumer level dataaccessed from the consumer data source 166B). Such granular data may beof particular interest to an investment company, for example, which maywant to target particular ZIP codes or households within a certain assetrange or above a certain asset threshold. However, as the map of FIG. 1shows, many of these ZIP9 codes include less than four households, andin some cases only one or two households. To protect the privacy ofthese households, while preserving the value in the data variance acrossZIP9 codes in this region, the micro-geographic aggregation system 100can analyze attributes of the ZIP9 codes in the area, identify potentialZIP9 codes for combination based on similarity, and rollup these ZIP9codes into a rollup group which meets a minimum number of householdsthreshold and with similar average assets and/or other attributes. Thus,for example in FIG. 1, ZIP9 codes 1231 (one household, $100K assets),1232 (two households, $110K average assets), and 1233 (three households,$90K average assets) may be rolled up to create a rollup group with acombined household total of six and an average asset attribute of $98K.ZIP9 codes 1237 (three households, $60K average assets) and 1238 (threehouseholds, $85K assets) also might be eligible for rollup if theminimum number of households is four; however, these ZIP9s may not berolled up together in some instances if, for example, the difference inaverage assets (e.g., $85k vs $60k) is relatively too high to beconsidered similar enough for rollup grouping. This determination willdepend on the particular embodiment or implementation of the rollupalgorithm and various criteria for filtering and rollup that areapplied. Or, in alternate example, ZIP9s 1233 and 1238 might beconsidered for possible rollup together since they have similar averageassets values ($90k and $85k respectively). FIG. 1 also illustrates acouple of ZIP9s (e.g., 1236 and 1239) with at least five households, andthus these ZIP9s may not need to be rolled-up or aggregated in caseswhere the minimum number of households is five. Instead, each of theseZIP9s may not be rolled up or, in one embodiment, may be assigned to arollup “group” which comprises only the respective ZIP9 (e.g., a“singleton”).

Examples of Methods Performed by a Micro-Geographic Aggregation System

FIGS. 2, 3, and 4 are flowcharts for various embodiments ofmicro-geographic aggregation system processes. In some implementations,the processes are performed by embodiments of the micro-geographicaggregation system 100 described with reference to FIG. 8 or by one ormore of its components, such as the geographic unit mapping engine 121,geographic unit rollup engine 122, and/or user interface module 123. Theexample scenarios are intended to illustrate, but not to limit, variousaspects of these systems and/or services. In one embodiment, theprocesses can be dynamic, with some procedures omitted and others added.In one example, multiple instances of the processes may occurconcurrently, for different micro-geographic aggregation systems.Depending on the embodiment, the methods may include fewer or additionalblocks and/or the blocks may be performed in an order different thanillustrated.

FIG. 2 is a flowchart illustrating one embodiment of a high-levelprocess 200 for combining one or more geographic units, such as ZIPcodes, together into a rollup group. The process 200 may be executed,for example, by the geographic unit rollup engine 122 of themicro-geographic aggregation system 100 of FIG. 8.

At block 205, the micro-geographic aggregation system 100 identifies,from a set of geographic data, one or more geographic units with anumber of households below a minimum threshold, such as within ageographic area of interest (e.g., a city or state that is the target ofa marketing campaign) to a company that is requesting geographicallyaggregated data. For example, the minimum threshold may be a valueprovided by a requesting entity or client using the micro-geographicaggregation system 100. The minimum threshold may also be a minimumrequired by government (e.g., Federal, state, municipality, etc.) rulesand regulations, including consumer privacy regulations. The one or moregeographic units may also be identified based on proximity to each otheror to a geographic unit that has been selected for potential rollup, orbased on other attributes as will be discussed in more detail herein. Asa specific example, at block 205 the micro-geographic aggregation systemmay find 5 ZIP9 codes with 5 or fewer households per ZIP9.

At block 210, the micro-geographic aggregation system 100 appliesfilters to the set of geographic data and the geographic units in orderto find candidates for potential rollup or aggregation. Filters may beapplied, for example, in order to remove those candidate ZIP codes whichare not close enough in proximity (e.g., geographic distance), notsimilar enough based one or more attributes of interest (e.g., averageassets per household, lifestyle characteristics or attributes asreflected in, for example, a MOSAIC® code, etc.), or on other criteria.In some embodiments, the filters may be accessed from the data sources166A or 166B, or otherwise be preconfigured by the micro-geographicaggregation system 100. In another embodiment, such as the exampleprocess 300 illustrated and described with reference to FIG. 3 herein,the filters may be accessed from or provided by a requesting entitysystem 164 as part of a rollup request.

At block 215, the micro-geographic aggregation system 100 scores each ofthe candidate geographic units in order to select the best candidate fora rollup group. Scoring of the candidate geographic units may beperformed a number of different ways depending on the embodiment orimplementation. The scoring may be performed relative to a target orbase reference geographic unit (or a rollup group), such that eachcandidate may be compared on the same basis. One example of how thescoring may be performed is discussed in further detail herein withrespect to block 435 of FIG. 4. In one embodiment, more than onecandidate may be selected. For example, to improve speed or efficiencyof processing, the two best scoring candidates may be selected forinclusion in a rollup group. Once the best scoring candidate(s) havebeen selected, the candidate(s) are combined with the base referencegeographic unit to form a new rollup group, or added to a base referencerollup group if one exists.

At block 220, the micro-geographic aggregation system 100 determineswhether the rollup group has a combined number of households below theminimum threshold. In response to a determination that the number ofhouseholds in the rollup group is still below the minimum threshold, theprocess 200 returns to block 205 to repeat block 205 to block 220. Inresponse to a determination that the number of households in the rollupgroup is above or equal to the minimum threshold, the process 200 canproceed to block 225.

At block 225, the micro-geographic aggregation system 100 provides therollup group as output, for example to another process of themicro-geographic aggregation system 100 or to a requesting entity system164. For example, after the rollup process is finished, a rollup ID (or“roll ID”) of “0” may be assigned to each ZIP9 that did not needgrouping (e.g., a singleton) to indicate that the singleton is notassociated with any rollup group. Then, the output may be provided in anumber of different formats. For example, in one embodiment, the listmay be provided as a list of ZIP9's and associated roll IDs, along withone or more attributes or metrics of interest for the ZIP9 (e.g.,average assets per household, number of households, MOSAIC® code, etc.).In another embodiment, the list may be provided as a list of roll IDswith a list of one or more associated ZIPs in the rollup group, alongwith one or more attributes or metrics of interest or related to therollup (e.g., average assets for the group, total number of householdsfor the group, MOSAIC® code(s) for the group, etc.). One example of arollup output is illustrated and described with reference to FIG. 6herein.

FIG. 3 is a flowchart illustrating one embodiment of a high-levelprocess 300 for combining one or more geographic units (e.g., ZIP9codes) together into a rollup group. The process 300 may be executed,for example, by the geographic unit rollup engine 122 of themicro-geographic aggregation system 100 of FIG. 8.

The process 300 involves similar routines as those described withreference to process 200 of FIG. 2, but presents alternatives which maybe implemented in some embodiments of the micro-geographic aggregationsystem 100. For example, process 300 involves receiving one or morefilter parameters or criteria, such as from a requesting entity system164, related to ZIP code attributes (e.g., at block 310). The filtersmay be applied in a similar process as described at block 210 of FIG. 2,but the example process 300 illustrates how these filters may be customfilters provided by a third party requesting entity, such as a companythat wishes to obtain aggregated or rolled-up ZIP9 data based on filtercriteria or business rules which are tailored to, for example, itsparticular type of business or its particular business requirements. Forexample, an investment or financial services firm might be interested infiltering ZIP9 data based on certain financial metrics like averageassets per household, average debt/liabilities per household, and thelike. Or, in another example, a credit account servicing firm (e.g. acredit card company) might be interested in filtering ZIP9 data based oncertain metrics like average credit score per household, average numberof pre-college or post-college children per household, and the like. Themicro-geographic aggregation system 100 may be configured to enable allsuch examples of custom filtering via the process 300.

In some embodiments, filters and/or filter criteria may not be used atall, for example depending on the number of candidate ZIP codesidentified for potential rollup. For example, if a small number ofcandidate geographic units are identified, it may be unnecessary orundesirable to filter the list and instead score each candidate in anunfiltered list. If a large number of candidate geographic units areidentified, it may be necessary or desirable to filter the list andscore only a subset of the candidates. For example, in some embodiments,the number of candidate geographic units may number in the hundreds,thousands, or more, and therefore filtering the list may be ofparticular benefit to improve speed and efficiency due to the overallcomplex and computationally intense rollup aggregation process. Tofurther illustrate these alternative embodiments, the process 300 ofFIG. 3 omits the optional filtering routine (e.g., applying filters asdescribed in other embodiments, such as at block 210 of FIG. 2 or atblock 430 of FIG. 4).

At block 305, the micro-geographic aggregation system 100 accesses alist of target geographic units and related attribute data for potentialrollup aggregation. In one embodiment, the list of target geographicunits may be accessed from geographic unit/mapping data source 166A.Related ZIP code attribute data may be accessed from the consumer datasource 166B, which may store for example consumer and/or socio-economicdata associated with the geographic units. In another embodiment, thelist of target geographic units may be provided by a requesting entity,for example as part of request to perform a rollup operation. The listof target geographic units may be, for example, one or more ZIP9 codes,each of which has a number of households below a threshold.

At block 310, the micro-geographic aggregation system 100 receives oneor more filter parameters related to the geographic units' attributedata. For example, the filters may be accessed from or provided by arequesting entity system 164 as part of a rollup request.

At block 315, the micro-geographic aggregation system 100 selects atarget geographic unit from the list for rollup aggregation. In oneembodiment, before the target geographic unit is selected from the list,the list may be sorted by one or more sorting criteria to facilitateprocessing of the list. For example, the list of targets may be sortedby the number of households in each target geographic unit with thesmallest number at the top of the list. This would then ensure that therollup algorithm would first attempt to rollup those geographic unitswith the smallest number of households.

At block 320, the micro-geographic aggregation system 100 determines arollup group comprising one or more geographic units for rollupaggregation with the target geographic units. This rollup process may beperformed, for example, by the process 400 illustrated and described inmore detail with reference to FIG. 4 herein.

At block 325, the micro-geographic aggregation system 100 determineswhether there are additional target geographic units in the list torollup. In response to a determination that there are additional targetgeographic units in the list to rollup, the process 300 returns to block315 and the micro-geographic aggregation system 100 selects the nexttarget from the list for rollup. If there are not additional targets inthe list to rollup, the process 300 may proceed to block 330.

At block 330, the micro-geographic aggregation system 100 provides therollup group or groups as output, for example to another process of themicro-geographic aggregation system 100 or to a requesting entity system164. This process is similar to the process described at block 225 ofprocess 200 shown in FIG. 2 herein.

FIG. 4 is a flowchart illustrating one embodiment of a process 400 forcombining one or more geographic units, such as ZIP9 codes, togetherinto a rollup group. The process 400 may be executed, for example, bythe geographic unit rollup engine 122 of the micro-geographicaggregation system 100 of FIG. 8. The process 400 may be for example asub-routine which is invoked as part of the process 300 of FIG. 3, forexample at block 320, to execute a particular embodiment of the rollupprocess. In particular, the process 400 includes several decision blocksand alternative approaches to perform the rollup depending on theunderlying ZIP code data, the number of ZIP codes found matching variousrollup conditions (e.g., proximity, granularity, filter criteria orconditions for attribute data associated with the geographic units, andso on). Depending on the particular embodiment, the process 400 may beperformed independently from the process 300, separately or in parallelto process multiple geographic unit rollups for a plurality of candidategeographic units.

At block 405, the micro-geographic aggregation system 100 determines oridentified one or more candidate geographic units (e.g., ZIP9 codes)within a geographic distance of a selected target ZIP code. In oneimplementation, the geographic distance may be set or preconfigured to acertain radius that may be optimal based on the geographic region of theselected target geographic unit. For example, the geographic distancemay be larger if the target is in a remote or rural geographic locationthat is likely to have a small number of candidates within a large area.Or, the geographic distance may be smaller if the target is in a denselypopulated geographic location that is likely to have a large number ofcandidates within a small area. The geographic distance may also bebased on other conditions such as the average number of geographic unitswithin different radius values. In one example embodiment, thegeographic distance may initially be set to 0.75 miles. Themicro-geographic aggregation system 100 may access data from geographicunit/mapping data source 166A in order to identify all ZIP9 codes withinthe geographic distance of the selected target ZIP9 code, and use theidentified ZIP9 codes as candidates for rollup or aggregation with theselected target ZIP9 code.

Finding all geographic units within a geographic distance of a selectedtarget geographic unit may be done in various ways. In one embodiment, atwo-step block search approach may be used to efficiently find all ZIP9sfor a given radius. First, given the latitude/longitude coordinates forthe lower left (A) and upper right corner (B) of the United States,create a grid with 1° latitude×1° longitude blocks. Then, for each 1×1block, find all the ZIP9's that belong to or fall within the block tocreate a mapping or index of ZIP9s-to-blocks. For ZIP9s which overlapmore than one block, the ZIP9 may be assigned to the block with thegreatest degree of overlap. This information may be saved for use insubsequent block searches and need not be repeated every time. Second,to find all ZIP9's within a certain distance of a given ZIP code, fromthe given ZIP code's latitude/longitude coordinates and radius R,calculate the latitude/longitude coordinates for the lower left (C) andupper right (D) corner of a bounding box on the grid. Then, the index ofthe blocks to which points C&D belong may be used to determine whichZIP9s are within the given radius, based on the mapping ofZIP9s-to-blocks created before. The initial search range could then beconfined to the only the blocks within the C&D index instead of thewhole universe of ZIP9's across the United States.

In one embodiment, at block 405 the micro-geographic aggregation system100 may also, or instead, determine one or more candidate ZIP9 rollupswithin the geographic distance of the selected target ZIP9 code. Thismay be the case, for example, if and when the process 400 iterativelyrepeats block 405 through block 445 to generate rollups dynamicallyusing a “local optimum” or “greedy” algorithm approach. For example, ona first iteration of the process 400, a candidate ZIP9 code list mightinclude three ZIP9 codes with a number of households equal to 1, 2, and3. At the end of a first iteration, the first candidate ZIP9 code may beselected for inclusion in a rollup with the selected target ZIP9 code,and the rollup may be added back to the candidate list for furtherpotential rollup (e.g., if the combined number of households in therollup is still less than the minimum threshold). Then, on a subsequentiteration of the process 400, the rollup may be included on thecandidate list and potentially be selected for inclusion with anothercandidate ZIP9 code or selected target ZIP9 code. In some embodiments,rollups may have been previously determined and stored along with otherZIP code/mapping data at data source 166A, and retrieved or accessed insubsequent executions of the rollup process 400.

Next at block 410, the micro-geographic aggregation system 100determines whether there is at least one candidate geographic unitidentified or found within the geographic distance at block 405. Inresponse to determining that there are no candidate ZIP codesidentified, the process 400 may proceed to block 415. In response todetermining that there is at least one candidate ZIP code identified,the process 400 may proceed to block 420.

At block 415, the micro-geographic aggregation system 100 enlarges thegeographic distance in order to locate at least one candidate geographicunit within a range of the selected target geographic unit. For example,in one embodiment the geographic distance may be increased to an amountequal to the original distance multiplied by the square root of 2 (orany other distance which may be optimal or otherwise desired). As withthe routine performed at block 405, the amount by which the geographicdistance is to be increased may depend on the geographic regionassociated with the selected target geographic unit. For example, for arural geographic region, the geographic distance may be increased by alarger amount in order to increase the likelihood of finding at leastone candidate. Once the geographic distance has been enlarged, theprocess 400 may return to block 405 and begin the rollup process again.The process 400 may repeat blocks 405, 410, and 415 iteratively anindeterminate amount of times until at least one candidate geographicunit is identified.

At block 420, the micro-geographic aggregation system determines whethertwo or more candidate geographic units identified or found within thegeographic distance at block 405. In response to determining that onlyone candidate ZIP code has been identified, the process 400 may proceedto block 425. If two or more one candidate ZIP codes have beenidentified, the process 400 may proceed to block 430.

At block 425, the micro-geographic aggregation system 100 selects theidentified candidate geographic unit for inclusion in a geographic unitrollup with the selected target ZIP code. The process 400 may thenproceed to block 445.

At block 430, the micro-geographic aggregation system 100 applies one ormore filter criteria to the candidate geographic units in order toremove candidates which do not satisfy the filters, based at least inpart on data associated with the candidate geographic units. In anembodiment the desired output of block 430 is a list of candidate ZIP9codes with a relatively high degree of similarity to the selected targetZIP9 code (e.g., filter out ZIP9s with low similarity based on certainattributes). Data associated with the candidate geographic units may beaccessed by the micro-geographic aggregation system 100 for example fromgeographic unit/mapping data sources 166A. In one embodiment, filtercriteria may be preconfigured or optimized, for example based oncriteria which may be known or learned over time (e.g., over multiplerollup processes/iterations) via a learning algorithm. In anotherembodiment, filter criteria may be selectable or configurable by an enduser, such as a requesting entity 164, and received by themicro-geographic aggregation system 100, for example via a userinterface, such as the user interface shown in FIG. 5A, which may beprovided as part of a web service.

A particular example of how filter criteria or rules may be applied toremove candidate geographic units from the list for potential inclusionin the rollup is provided as follows. Consider a scenario in which anaverage assets per household is the target variable, and a MOSAIC® codeis the explanatory variable. For the average assets variable, a filteror rule may be applied to remove any candidate ZIP9 codes which are notwithin a certain percent (e.g., 20%) or range (e.g., plus or minus$5,000) of the average assets associated with the target ZIP code. Insome instances the rollup algorithm may be configured to automaticallytreat asset values below a minimum amount as the minimum amount (e.g.,treat any asset value below $5,000 as $5,000 for purposes of rollupaggregation). For the MOSAIC® code variable, a filter may be applied toremove any candidate ZIP9 codes which do not have the same category orsub-category (e.g., the same letter group) of the MOSAIC® codeassociated with the target ZIP9 code. In some instances the rollupalgorithm may be configured to automatically remove any candidate ZIP9code which does not have an associated MOSAIC® code. The geographicdistance may also be considered another type of filter criteria or rulethat is based on proximity (e.g., initially only candidate ZIP9 codeswithin a certain radius of the target ZIP9 code may be considered forpotential rollup).

Other types of filter criteria or rules may be applied to any otherattribute of consumer and or socio-economic data associated withgeographic units, depending on the embodiment, or on the requirements ofthe requesting entity 164. Other filter criteria may be based on, forexample, average credit scores per household, average number of childrenper household, average income per household, average liabilities perhousehold, average number of identity theft or fraud incidents, and anyother attribute described herein including socio-economic demographics,lifestyle segments, and the like. For example, a requesting entity maywish to customize an offer to enroll in a credit identity monitoringservice, but only target those households in ZIP9 codes corresponding torelatively higher incidents of identity theft or fraud. Thus in thisexample filter criteria may be applied to remove candidate ZIP9 codesfrom potential rollup which have a lower average of identity theft orfraud incidents, since the corresponding households fall outside therange of the desired target demographic.

Once the initial candidate list has been filtered according to theprocess at block 430 (or a similar filtering process), then at block435, the micro-geographic aggregation system 100 scores the remainingcandidates, based at least in part on data associated with the candidategeographic units. Depending on the embodiment, the scoring may beperformed using any type of scoring algorithm which may be preferred. Insome embodiments the scoring algorithm may be selected or provided by arequesting entity 164, which may have one or more business rules orpreferences for how geographic unit similarity or dissimilarity is to beevaluated, assessed, and/or scored. In general, any scoring routinewhich analyzes and/or evaluates the attributes associated with thecandidate geographic units, optionally performs some tradeoffcalculations and weighting to determine an output score for eachcandidate geographic unit in a consistent manner to support reliabledecision making and “best score” selection may be used. Scores may becalculated in any format including a numeric score, a letter score orgrade (e.g., A-F), a percentage, a range, and the like. In oneembodiment, a scoring algorithm may employ a weighted sum of exponentialfunctions that penalize (i) the distance between two candidategeographic units, (ii) a difference (or a degree of difference) in thevalues of the respective target variables, (iii) a difference (or adegree of difference) in the values for other descriptive variables, and(iv) a difference between the total number of households and the targetor minimum number of households threshold to maintain privacy.

Continuing the example with respect to assets and MOSAIC® codes, ascoring algorithm may be executed by the micro-geographic aggregationsystem 100 which calculates a score for a candidate geographic unitbased on desired attributes (e.g., assets and MOSAIC® code similarity,proximity, granularity, etc.) and/or tradeoffs. For example, in oneembodiment, the score calculation may place more importance on factorsother than distance as the distance increases (e.g., the greater thedistance between geographic units, the more desirable it becomes to havea higher degree of similarity based on other attributes). In oneexample, a tradeoff may be implemented to sacrifice 5% of assetdifference in exchange for being a certain distance (e.g., 100 meters)closer in proximity. Other examples may include: having the exact sameMOSAIC® code or segment may get more weight; being in the same apartmentcomplex may get more weight; tradeoff a certain distance in proximity inexchange for having the number of households in the candidate geographicunit reduced or increase by a certain amount in order to exactly meet aminimum household threshold; and balancing the number of candidategeographic units grouped into a single rollup in order to try andexactly meet a minimum household threshold. Exact tradeoffs implementedmay vary depending on the attributes being analyzed and the end desiredgoal.

At block 440, the micro-geographic aggregation system 100 selects thebest scoring candidate geographic unit for inclusion in a geographicunit rollup with the selected target geographic unit. Depending on theembodiment, the best scoring candidate may be a low scoring candidate(e.g., if the score is a penalty score where low scores are preferredover high scores), or a high scoring candidate, or any other variationor rule for determining which score is a “best” score. When thecandidate is selected for inclusion in the rollup, the underlyingattribute data associated with the geographic units in the rollup may beaggregated, combined, or averaged such that the underlying statisticalvariance in the data set is preserved to the greatest extent possiblewith respect to non-rolled up geographic units. The process 400 may thenproceed to block 445.

At block 445, the micro-geographic aggregation system 100 determineswhether the number of households in the rollup group is below theminimum threshold. In response to determining that the number ofhouseholds in the rollup group is below the minimum threshold, theprocess 400 may return to block 405 and repeat block 405 to block 445 inorder to continue the rollup process until the rollup group includesenough households to satisfy the minimum threshold. In response todetermining that the number of households in the rollup group is greaterthan or equal to the minimum threshold, the process 400 may proceed toblock 450.

At block 450, the micro-geographic aggregation system 100 provides therollup group as output, for example to another process of themicro-geographic aggregation system 100 or to a requesting entity system164. This process is similar to the process described at block 225 ofprocess 200 shown in FIG. 2 herein.

In one embodiment, the process 400 may be executed by themicro-geographic aggregation system 100 an indeterminate number of timesfor a set of selected target geographic units, with each iteration ofthe process 400 generating a rollup group for each selected targetgeographic unit in the list. In one embodiment the process 400 may beexecuted or performed in parallel for each selected target geographicunit in the list. This may be possible in instances where the selectedtargets each comprise a separate, non-overlapping set of candidategeographic units which may be rolled up or aggregated with respect toeach selected target.

In some embodiments, it may be possible that the set of selected targetgeographic units may contain zero or only one candidate geographic unitfor rollup aggregation, or that a generated rollup group for a selectedtarget geographic unit has a combined number of households less than theminimum even if all candidate geographic units associated with aselected target are included in the rollup group. Thus, themicro-geographic aggregation system 100 may be configured to account forthese possible scenarios by combining or rolling up one or more selectedtarget geographic units with each other. This combination of selectedtarget geographic units may be performed before or after the process 400is executed. The combination of selected target geographic units in thisway may be performed using a process similar to the process 400, or by aseparate process, or may even be configured manually, depending on theembodiment.

The process 400 may also be performed for a plurality of target ZIPcodes for potential rollup (e.g., as received in a batch request)substantially in parallel and substantially in real time.

Sample User Interfaces

FIGS. 5A and 5B illustrate example user interfaces (“UIs”) as used inone or more embodiments of a micro-geographic aggregation system. Thesample user interfaces may be displayed, for example, via a web browseror standalone application, and may be provided, for example, as a webservice by the micro-geographic aggregation system 100. However, in someembodiments, the sample user interfaces shown in FIGS. 5A and 5B mayalso be displayed on a suitable computer device, such as a personalcomputer, desktop, laptop, cell/smart phone, tablet, or portablecomputing device, and are not limited to the samples as describedherein. The user interfaces are examples of only certain features that amicro-geographic aggregation system may provide. In other embodiments,additional features may be provided and they may be provided usingvarious different user interfaces and software code. Depending on theembodiment, the user interfaces and functionality described withreference to FIGS. 5A and 5B may be provided by software executing on acomputing device, by a micro-geographic aggregation system locatedremotely that is in communication with the computing device via one ormore networks, and/or some combination of software executing on thecomputing device and the micro-geographic aggregation system.

FIG. 5A illustrates an example user interface 500A which enables an enduser, such as a third party requesting entity, to submit customizedrollup filters and data requests, as generated in one embodiment of themicro-geographic aggregation system 100. These filters may be applied bythe micro-geographic aggregation system 100 “on-the-fly” to underlyingconsumer-level and/or ZIP attribute data to dynamically perform customrollups in response to the data request from the end user. As shown inFIG. 5A, the user is presented with options to specify a number ofthresholds 501, a ZIP code/geographic region 502, and an average assets503 on which to filter candidate ZIPs. The user interface 500A mayadditionally support candidate filtering and/or scoring criteria basedon any other data attribute described herein which are not illustratedin FIG. 5A, such as socio-economic attributes, demographics, lifestylesegments, behavioral/attitudinal, credit data-related attributes, and soon. When the user is ready to request a rollup, he/she selects therollup option 504, or the user may choose to clear 505 and start over.

In some embodiments the user may be able to enter more than one targetZIP code to be rolled up at one time (e.g. the micro-geographicaggregation system 100 may receive multiple ZIP codes for rollupaggregation in batches).

FIG. 5B illustrates an example user interface 500B which enables an enduser, such as a third party requesting entity, to view rollup mapsand/or output lists generated in response to a rollup request, asgenerated in one embodiment of the micro-geographic aggregation system100. User interface 500B shows an output rollup grid 506 and a rollupmap 509. An example of the output rollup grid 506 is illustrated anddescribed in more detail with reference to FIG. 6 herein. The rollup map509 may provide a visual representation of the rolled-up ZIP groups forthe selected geographic region or ZIP code. The rollup groupings may beindicated for example by different colored lines, lines of differentstyles or thickness (e.g., dashed, dotted, bold), or any other visualrepresentation which may be used to convey which rollup group to whicheach ZIP9 in the area has been assigned. The visualization can provideextra value in that the end user can quickly see how the ZIP9 codes havebeen grouped together, which need not always end up on adjacent ZIP9sbeing grouped together in the same rollup.

The user interface 500B may also provide an option for the user toexport the rollup results 507, or the user may choose to start over 508and return to the user interface 500A.

Sample Rollup Output

FIG. 6 illustrates an example geographic unit rollup output which may beprovided by the rollup processes described herein. The examplegeographic unit rollup output may be provided, for example, to arequesting entity system 164, in different output formats including butnot limited to a batch file format (e.g., a text file or a table) and/ora user interface which displays the rollup output such as the userinterface 500B shown in FIG. 5B.

The example geographic unit rollup output of FIG. 6 illustrates a sampleoutput with respect to the asset and MOSAIC® code example discussedthroughout this disclosure. In particular, the table 600 includesinformation indicating which ZIP9 codes 601 have been grouped together,an address range (from 602 and to 603) corresponding to each ZIP9 code,a number of households 604 for each ZIP9, a MOSAIC® code 605 for eachZIP9, a roll ID 606 for the rollup group, a total number of households607 for each rollup group, and an average assets 608 for the rollupgroup.

In one embodiment where the table 600 is included as part of a visualoutput display such as the one illustrated in FIG. 5B, rows of the table600 may be color-coded or otherwise formatted to match the rollupgroupings displayed on a map. In one example user interface, in responseto the user moving a pointer or mouse (or touching a portion of the userinterface for touch-screen enabled devices) over a row in the table 600,the rollup groupings displayed on the map may be highlighted to furtherillustrate to the end user where the ZIP codes in the rollup group aregeographically on the map. Similarly rows in the table 600 may behighlighted in response to the user moving a pointer (or touching thescreen) relative to ZIP code segments on the map.

Mapping Geographic Unit Data

FIG. 7 illustrates an example of how different map data sources, mayhave different and/or conflicting map latitude/longitude (“lat/long”)coordinates for geographic units, such as ZIP codes, which may need tobe reconciled for a geographic unit rollup process. For example, asshown in the map 700 of FIG. 7, for an example ZIP9 defined by a streetaddress range with endpoints at points “B” and “E”, one data source mayindicate that the latitude and longitude of the center point of the linesegment connecting the endpoints is at point “A”, a second data sourcemay indicate that center point corresponds to point “C”, and a thirddata source may indicate that center point corresponds to point “D”. Insome embodiments, it may be beneficial to improve the accuracy of thelocation of the ZIP9 codes, in particular for ZIP9 codes which cover asmall area or only a few households where minor differences in lat/longcoordinates can make the difference as to which other ZIP9 codes wouldbe most suitable for combination. For example, with reference to theprocess 400 illustrated and described in FIG. 4, finding all ZIP codeswithin a geographic distance or radius of a target ZIP code may requireusing an accurate or verified center point and/or endpoints for thetarget ZIP code.

One embodiment of the micro-geographic aggregation system 100 provides aprocess or mechanism for accessing map data from different map datasources, combining the map data and reconciling differences ordiscrepancies in order to generate a single, consolidated mapping ofZIP9 codes to latitude/longitude coordinates. The latitude/longitudemapping process may be performed, for example, by geographic unitmapping engine 121 of the micro-geographic aggregation system 100. Theconsolidated mapping of ZIP9 codes to lat/long coordinates may be storedin one of the data sources 166 and accessed by the micro-geographicaggregation system 100 for use in the rollup processes described herein,such as the processes described with reference to FIGS. 2, 3, and 4.

Latitude/longitude selection for ZIP9 map data reconciliation may beimplemented a number of ways. In some embodiments, a confidence code orlevel may be assigned to a set of selected latitude/longitudecoordinates to provide an indication of how accurate the selectedlatitude/longitude coordinates may be, based on the underlying sourcedata. For example, a confidence level of 1 may be assigned if thelat/long coordinates are confirmed for a base reliable source (e.g., USCensus/TIGER) relative to a secondary source. The lat/long coordinatesmay be confirmed if, for example, the distance between the two sourcesis less than a certain distance such as 0.1 mile. A confidence level of2 may be assigned if only a base reliable source is available, but thelat/long coordinates do not agree with a secondary source. A confidencelevel of 3 may be assigned if more than two sources are available orhave lat/long coordinates for the ZIP, but none of them are pairwiseclose. A confidence level of 4 may be assigned if only a base reliablesource is available but no secondary source data is available to confirmthe base source. A confidence level of 5 may be assigned if onlysecondary sources are available and/or used to approximate the lat/longcoordinates for the ZIP. Additional or fewer confidence levels may beimplemented depending on the different types of source data, level orconfidence of accuracy, and so on. Other types of confidence scores mayalso be used, including letter scores, ranges, percentages, and so on.

Returning to the example illustrated in FIG. 7, according to oneembodiment of the latitude/longitude mapping process implemented by themicro-geographic aggregation system 100, point C may be selected as thecenter point of the ZIP9 as follows. Suppose the most reliable sourceindicates that the center point of the ZIP9 is at point C; a secondarysource indicates that the center point of the ZIP9 is point A; and anadditional reliable source indicates that the center point of the ZIP9is point D. Point A may be eliminated from consideration on the basis ofbeing a secondary source and too far from points C and D. Point C may beselected because its source is considered more reliable, though Point Dis too far away from Point C to confirm its accuracy. Thus Point C maybe selected as the most accurate center point for this ZIP9 based on theavailable source data. Further in this example, a confidence level of 3may be applied since all three data sources are available but none ofthem are within the specified threshold distance of each other.

Example System Implementation and Architecture

FIG. 8 is a block diagram of an example implementation of amicro-geographic aggregation system 100 in communication with a network160 and various systems, such as requesting entity system(s) 164 andgeographic unit/mapping data source(s) 166A consumer data source(s)166B. The micro-geographic aggregation system 100 may be used toimplement systems and methods described herein.

The micro-geographic aggregation system 100 includes, for example, apersonal computer that is IBM, Macintosh, or Linux/Unix compatible or aserver or workstation. In one embodiment, the micro-geographicaggregation system 100 comprises a server, a laptop computer, a smartphone, a personal digital assistant, a kiosk, or an media player, forexample. In one embodiment, the exemplary micro-geographic aggregationsystem 100 includes one or more central processing unit (“CPU”) 105,which may each include a conventional or proprietary microprocessor. Themicro-geographic aggregation system 100 further includes one or morememory 130, such as random access memory (“RAM”) for temporary storageof information, one or more read only memory (“ROM”) for permanentstorage of information, and one or more mass storage device 120, such asa hard drive, diskette, solid state drive, or optical media storagedevice. Typically, the modules of the micro-geographic aggregationsystem 100 are connected to the computer using a standard based bussystem 180. In different embodiments, the standard based bus systemcould be implemented in Peripheral Component Interconnect (“PCI”),Microchannel, Small Computer System Interface (“SCSI”), IndustrialStandard Architecture (“ISA”) and Extended ISA (“EISA”) architectures,for example. In addition, the functionality provided for in thecomponents and modules of micro-geographic aggregation system 100 may becombined into fewer components and modules or further separated intoadditional components and modules.

The micro-geographic aggregation system 100 is generally controlled andcoordinated by operating system software, such as Windows XP, WindowsVista, Windows 7, Windows 8, Windows Server, Unix, Linux, SunOS,Solaris, iOS, Blackberry OS, or other compatible operating systems. InMacintosh systems, the operating system may be any available operatingsystem, such as MAC OS X. In other embodiments, the micro-geographicaggregation system 100 may be controlled by a proprietary operatingsystem. Conventional operating systems control and schedule computerprocesses for execution, perform memory management, provide file system,networking, I/O services, and provide a user interface, such as agraphical user interface (“GUI”), among other things.

The exemplary micro-geographic aggregation system 100 may include one ormore commonly available input/output (I/O) devices and interfaces 110,such as a keyboard, mouse, touchpad, and printer. In one embodiment, theI/O devices and interfaces 110 include one or more display devices, suchas a monitor, that allows the visual presentation of data to a user.More particularly, a display device provides for the presentation ofGUIs, application software data, and multimedia presentations, forexample. The micro-geographic aggregation system 100 may also includeone or more multimedia devices 140, such as speakers, video cards,graphics accelerators, and microphones, for example.

In the embodiment of FIG. 8, the I/O devices and interfaces 110 providea communication interface to various external devices. In the embodimentof FIG. 5, the micro-geographic aggregation system 100 is electronicallycoupled to a network 160, which comprises one or more of a LAN, WAN,and/or the Internet, for example, via a wired, wireless, or combinationof wired and wireless, communication link 115. The network 160communicates with various computing devices and/or other electronicdevices via wired or wireless communication links.

According to FIG. 8, in some embodiments information may be provided tothe micro-geographic aggregation system 100 over the network 160 fromone or more geographic unit/mapping data sources 166A and/or consumerdata source 166B. The geographic unit/mapping data source(s) 166A andconsumer data sources 166B may include one or more internal and/orexternal data sources. The geographic unit/mapping data source(s) 166Amay store, for example, geographic data including but not limited tocensus tract data, ZIP codes, ZIP5s, ZIP7s, ZIP9s, street addressranges, grid-based geographic regions corresponding to a map, and dataotherwise describing or defining any other finite geographic area orlocation. In some embodiments, grid-based geographic regions maycomprise or correspond to areas of certain population density, which maythen be grouped or aggregated in a variable resolution grid. Theconsumer data sources 166B may store, for example, credit bureau data(for example, credit bureau data from File One℠) and/or other consumerdata. Consumer data sources 166B may also store geographic leveldemographics that include one or more models, such as models thatidentify lifestyle and/or socio-economic attributes associated with ageographic location (e.g., MOSAIC® segmentation and/or codes) and/orbehavioral/attitudinal/psychographic attributes associated with ageographic location (e.g., TrueTouch℠ Touch Points segmentation). Insome embodiments, one or more of the databases or data sources may beimplemented using a relational database, such as Sybase, Oracle,CodeBase and Microsoft® SQL Server as well as other types of databasessuch as, for example, a flat file database, an entity-relationshipdatabase, and object-oriented database, and/or a record-based database.

In the embodiment of FIG. 8, the micro-geographic aggregation system 100includes a geographic unit mapping engine 121, a geographic unit rollupengine 122, and a user interface module 123 that may be stored in themass storage device 120 as executable software codes that are executedby the CPU 105. This and other modules in the micro-geographicaggregation system 100 may include, by way of example, components, suchas software components, object-oriented software components, classcomponents and task components, processes, functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuitry, data, databases, data structures, tables, arrays,and variables. In the embodiment shown in FIG. 8, the micro-geographicaggregation system 100 is configured to execute the geographic unitmapping engine 121, the geographic unit rollup engine 122, and/or theuser interface module 123 to perform the various methods and/orprocesses for geographic unit rollup, combination, and/or aggregationsas described herein (such as the processes described with respect toFIGS. 2, 3, and 4 herein).

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, Lua, C or C++. A software modulemay be compiled and linked into an executable program, installed in adynamic link library, or may be written in an interpreted programminglanguage such as, for example, BASIC, Perl, or Python. It will beappreciated that software modules may be callable from other modules orfrom themselves, and/or may be invoked in response to detected events orinterrupts. Software modules configured for execution on computingdevices may be provided on a computer readable medium, such as a compactdisc, digital video disc, flash drive, or any other tangible medium.Such software code may be stored, partially or fully, on a memory deviceof the executing computing device, such as the micro-geographicaggregation system 100, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Themodules described herein are preferably implemented as software modules,but may be represented in hardware or firmware. Generally, the modulesdescribed herein refer to logical modules that may be combined withother modules or divided into sub-modules despite their physicalorganization or storage.

Other Embodiments

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The code modules may be storedon any type of non-transitory computer-readable medium or computerstorage device, such as hard drives, solid state memory, optical disc,and/or the like. The systems and modules may also be transmitted asgenerated data signals (e.g., as part of a carrier wave or other analogor digital propagated signal) on a variety of computer-readabletransmission mediums, including wireless-based and wired/cable-basedmediums, and may take a variety of forms (e.g., as part of a single ormultiplexed analog signal, or as multiple discrete digital packets orframes). The processes and algorithms may be implemented partially orwholly in application-specific circuitry. The results of the disclosedprocesses and process steps may be stored, persistently or otherwise, inany type of non-transitory computer storage such as, e.g., volatile ornon-volatile storage.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and subcombinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list. Conjunctivelanguage such as the phrase “at least one of X, Y and Z,” unlessspecifically stated otherwise, is otherwise understood with the contextas used in general to convey that an item, term, etc. may be either X, Yor Z. Thus, such conjunctive language is not generally intended to implythat certain embodiments require at least one of X, at least one of Yand at least one of Z to each be present.

While certain example embodiments have been described, these embodimentshave been presented by way of example only, and are not intended tolimit the scope of the disclosure. Thus, nothing in the foregoingdescription is intended to imply that any particular element, feature,characteristic, step, module, or block is necessary or indispensable.Indeed, the novel methods and systems described herein may be embodiedin a variety of other forms; furthermore, various omissions,substitutions and changes in the form of the methods and systemsdescribed herein may be made without departing from the spirit of theinventions disclosed herein. The accompanying claims and theirequivalents are intended to cover such forms or modifications as wouldfall within the scope and spirit of certain of the inventions disclosedherein.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure.

What is claimed is:
 1. A computer-implemented method of displayinghousehold information relating to and facilitating presentation of theinformation in a way that satisfies a minimum threshold of householdgranularity on a graphical user interface, the method comprising: undercontrol of a computing device configured with specificcomputer-executable instructions: receiving, from a requesting entity,at least one indication associated with a geographic area; displaying amap of the geographic area based on the at least one indication;receiving, from the requesting entity, a minimum threshold indicating aquantity of households required in aggregated groups of households, theminimum threshold indicating the minimum number of individual householdsincluded in the aggregated groups; accessing a list of one or moretarget geographic units and related attribute data, wherein each of thetarget geographic units includes one or more households associated witha location on the displayed map; determining one or more combinablegeographic units within a particular geographic distance of a targetgeographic unit, the target geographic unit having a target number ofhouseholds within the target geographic unit; identifying one or morecandidate geographic units for potential aggregation with the targetgeographic unit; applying one or more filter criteria to the one or morecandidate geographic units to remove candidate geographic units that donot meet the one or more filter criteria; determining, for eachremaining candidate geographic unit after application of the filter: ascore based at least in part on similarity of a household data attributeassociated with the remaining candidate geographic unit and the targetgeographic unit; determining a best candidate number of householdsassociated with a best candidate geographic unit having a highest score;updating an aggregated group to include the best candidate geographicunit and the target geographic unit; determining an aggregated group sumas a sum of the best candidate number of households and the targetnumber of households; determining whether the aggregated group sum isbelow the minimum threshold; and automatically repeating until theaggregated group sum is not below the minimum threshold: determining abest candidate geographic unit for aggregation into the aggregatedgroup, the best candidate geographic unit having a best score; updatingthe aggregated group sum to include a best candidate number ofhouseholds; determining whether the aggregated group sum is below theminimum threshold; or in response to determining that the aggregatedgroup sum is not below the minimum threshold: determining an aggregatedvalue of household data attributes of households within the aggregatedgroup; associating the aggregated group with a distinct visualindicator; determining locations of each of the geographic units withinthe aggregated group on the map; and updating the map to depict thedistinct visual indicator at each of the determined locations on themap, wherein the individually identifiable household data attributes ofthe individual households in the aggregated group are concealed.
 2. Thecomputer-implemented method of claim 1, wherein identifying one or morecandidate geographic units for potential aggregation with the targetgeographic unit comprises: accessing geographic attribute dataassociated with a target geographic unit and one or more candidategeographic units, the geographic attribute data accessed from a physicaldata store storing geographic data that includes household dataattributes associated with respective geographic units, the householddata attributes indicating at least a number of households associatedwith respective geographic units; determining a geographic location forthe target geographic unit, based at least in part on accessedgeographic unit attribute data; and identifying the one or morecandidate geographic units, wherein each candidate geographic unit iswithin a radial distance of the geographic location, based at least inpart on geographic data associated with the respective candidategeographic unit.
 3. The computer-implemented method of claim 1, whereinthe predetermined geographic distance comprises a latitude longitudebounded area.
 4. The computer-implemented method of claim 1, wherein thehousehold data attributes include one or more socio-economic and/orlifestyle segment attributes.
 5. The computer-implemented method ofclaim 4, wherein the socio-economic attributes include an average assetvalue per household within the respective geographic unit.
 6. Thecomputer-implemented method of claim 1, wherein each candidategeographic unit is a ZIP+4 code.
 7. The computer-implemented method ofclaim 1, further comprising: receiving one or more additional filtercriteria from the requesting entity.
 8. The computer-implemented methodof claim 1, wherein the one or more additional filter criteria arereceived from the requesting entity via a web user interface, andwherein the web user interface provides one or more input fieldsenabling the requesting entity to configure custom filter criteria foridentifying the aggregated group.
 9. The computer-implemented method ofclaim 1, wherein determining the score for each remaining candidategeographic unit further comprises: identifying one or more targetattributes on which to analyze the similarity of each remainingcandidate geographic unit to the target geographic unit, wherein thetarget attributes are selected from the household data attributes;calculating, for each remaining candidate geographic unit, a weightedsimilarity of the remaining candidate geographic unit to the targetgeographic unit based on a comparison of the target attributes; anddetermining the score based at least in part on the weighted similarity.10. The computer-implemented method of claim 9, wherein the weightedsimilarity is calculated based at least in part on a calculatedgeographic distance between the remaining candidate geographic unit andthe target geographic unit.
 11. A computer system for displayinghousehold information relating to and facilitating presentation of theinformation in a way that satisfies a minimum threshold of householdgranularity on a graphical user interface, the system comprising: anelectronic data store configured to at least store geographic dataassociated with each of a plurality of geographic units; and a computingsystem comprising one or more hardware computing devices, said computingsystem in communication with the electronic data store and configured toat least: receive, from a requesting entity, at least one indicationassociated with a geographic area; display a map of the geographic areabased on the at least one indication; receive, from the requestingentity, a minimum threshold indicating a quantity of households requiredin aggregated groups of households, the minimum threshold indicating theminimum number of individual households included in the aggregatedgroups; determine one or more combinable geographic units within aparticular geographic distance of a target geographic unit, the targetgeographic unit having a target number of households within the targetgeographic unit; identify one or more candidate geographic units forpotential aggregation with the target geographic unit; apply one or morefilter criteria to the one or more candidate geographic units to removecandidate geographic units that do not meet the one or more filtercriteria; determine, for each remaining candidate geographic unit afterapplication of the filter: a score based at least in part on similarityof the household data attributes associated with the remaining candidategeographic unit and the target geographic unit; determine a bestcandidate number of households associated with a best candidategeographic unit having a highest score; update an aggregated group toinclude the best candidate geographic unit and the target geographicunit; determine an aggregated group sum as a sum of the best candidatenumber of households and the target number of households; determinewhether the combined number of households associated with the aggregatedgroup sum is below the minimum threshold; and automatically repeat untilthe aggregated group sum is not below the minimum threshold: determine anext best candidate geographic unit for aggregation into the aggregatedgroup, the next best candidate geographic unit having a next best score;update the aggregated group sum to include a next best candidate numberof households; determine whether the aggregated group sum is below theminimum threshold; or in response to determining that the aggregatedgroup sum is not below the minimum threshold: determine an aggregatedvalue of the household data attributes of households within theaggregated group; associate the aggregated group with a distinct visualindicator; determine locations of each of the geographic units withinthe aggregated group on the map; and update the map to depict thedistinct visual indicator at each of the determined locations on themap, wherein the individually identifiable household data attributes ofthe individual households in the aggregated group are concealed.
 12. Thecomputer system of claim 11, wherein each candidate geographic unit is aZIP+4 code.
 13. The computer-implemented method of claim 1, wherein theat least one indication is a ZIP code or a city name.
 14. Thecomputer-implemented method of claim 1, wherein the distinct visualindicators are distinguished by different colors, pattern, thickness, orsize.
 15. Non-transitory physical computer storage comprisingcomputer-executable instructions stored thereon that, when executed by ahardware processor, are configured to perform operations comprising:receiving, from a requesting entity, at least one indication associatedwith a geographic area; displaying a map of the geographic area based onthe at least one indication; receiving, from the requesting entity, aminimum threshold indicating a quantity of households required inaggregated groups of households, the minimum threshold indicating theminimum number of individual households included in the aggregatedgroups; accessing a list of one or more target geographic units andrelated attribute data, wherein each of the target geographic unitsincludes one or more households associated with a location on thedisplayed map; determining one or more combinable geographic unitswithin a particular geographic distance of a target geographic unit, thetarget geographic unit having a target number of households within thetarget geographic unit; identifying one or more candidate geographicunits for potential aggregation with a target geographic unit; applyingone or more filter criteria to the one or more candidate geographicunits to remove candidate geographic units that do not meet the one ormore filter criteria; determining, for each remaining candidategeographic unit after application of the filter: a score based at leastin part on similarity of a household data attributes associated with theremaining candidate geographic unit and the target geographic unit;determining a best candidate number of households associated with a bestcandidate geographic unit having a highest score; updating an aggregatedgroup to include the best candidate geographic unit and the targetgeographic unit; determining an aggregated group sum as a sum of thebest candidate number of households and the target number of households;determining whether the aggregated group sum is below the minimumthreshold; and automatically repeating until the aggregated group sum isnot below the minimum threshold: determining a next best candidategeographic unit for aggregation into the aggregated group, the next bestcandidate geographic unit having a next best score; updating theaggregated group sum to include a next best candidate number ofhouseholds; determining whether the aggregated group sum is below theminimum threshold; or in response to determining that the aggregatedgroup sum is not below the minimum threshold: determining an aggregatedvalue of the household data attributes of households within theaggregated group; associating the aggregated group with a distinctvisual indicator; determining locations of each of the geographic unitswithin the aggregated group on the map; and updating the map to depictthe distinct visual indicator at each of the determined locations on themap, wherein the individually identifiable household data attributes ofthe individual households in the aggregated group are concealed.
 16. Thenon-transitory physical computer storage of claim 15, wherein thehousehold data attributes include one or more socio-economic and/orlifestyle segment attributes.
 17. The non-transitory physical computerstorage of claim 15, wherein each candidate geographic unit is a ZIP+4code.